Updates, guides, and insights from the NanoGPT team
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86 posts found for 'models'
Yi Lightning is a new, Chinese-created model that is difficult to access. NanoGPT has it available.
Overview of all the updates to NanoGPT in October 2024
Explaining why we prefer to take payments in Nano rather than through credit cards, including the benefits of zero fees and instant settlements.
Monitor, automate error handling, version models, optimize resources, and protect data to keep hybrid AI workflows reliable.
Focused dashboards that track engagement, efficiency, and costs are the difference between wasted AI spend and measurable business impact.
Pin packages, models, and Docker images to ensure reproducible, secure AI deployments—commit lockfiles, verify hashes, and scan for vulnerabilities.
Guide to building supervised churn models: collect and clean data, engineer features, train Logistic/RandomForest/XGBoost, and evaluate with recall and F1.
Practical guidelines for testing AI models: define objectives, build golden datasets, run edge-case and adversarial tests, version control, and monitor drift.
Guide to profiling LLM latency: measure TTFT, TPOT, and ITL; use PyTorch, Nsight, and tracing; optimize batching, quantization, and memory bandwidth.
Monitor AI models to catch silent failures—track hallucinations, data drift, latency, token costs, set alerts, and automate retraining.
